Research on object detection for small objects in agriculture: taking red bayberry as an example

Shan Hua; Kaiyuan Han; Shuangwei Li; Minjie Xu; Shouyan Zhu; Zhifu Xu

doi:10.1117/12.3014464

9 January 2024 Research on object detection for small objects in agriculture: taking red bayberry as an example

Shan Hua, Kaiyuan Han, Shuangwei Li, Minjie Xu, Shouyan Zhu, Zhifu Xu

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129692B (2024) https://doi.org/10.1117/12.3014464
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

With the continuous improvement of intelligent management level in red bayberry orchards, the demand for automatic picking and automatic sorting is becoming increasingly apparent. The prerequisite for achieving these automated processes is to quickly identify the maturity of red bayberries by object detection. In this study, we classified red bayberry into 8 levels of maturity and achieved an object detection precision of 88.9%. We used a fast object detection model, combined with small object optimization methods and small feature extraction layers to get higher precision.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shan Hua, Kaiyuan Han, Shuangwei Li, Minjie Xu, Shouyan Zhu, and Zhifu Xu "Research on object detection for small objects in agriculture: taking red bayberry as an example", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129692B (9 January 2024); https://doi.org/10.1117/12.3014464

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